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Learning has become essential for many companies whose manufacturing environment, where tasks have increased in complexity, requires a workforce of specific skills. Training of workers and learning-by-doing reduce the unit cost as the production rate speeds and improve product and process quality. Better quality means less rework and machine downtime. All these aspects indirectly influence energy efficiency, which has become a concern for manufacturing companies due to the stringent targets imposed by legislators, the significance of energy costs, and the increasing customers’ awareness about the environment. A company, however, could invest in some alternatives to improve its energy efficiency. This paper, therefore, proposes a lot sizing model for a manufacturing company that includes the relevant learning outcomes, directly and indirectly, affecting its energy efficiency. The main result of the study is to show how learning in production and energy efficiency affects each other and the optimal lot size quantity.

Incorporation of sustainable management for the rework of defective items brings long lasting benefits. In global business, there are situations when the products are procured from a global supplier. There are chances that the received lot may contain a fraction of imperfect products. These imperfect products are still valuable and can be repairable to save the environment. It is sustainable to repair imperfect items in a local repair store as compared to sending it back to the supplier. The cost of carbon emissions is also incorporated in the function to incorporate the environmental impact on total profit. Meanwhile, the supplier also offers a multi-trade-credit-period to the buyer. The developed model is sustainable and reduces the environmental impact as well as benefits for interim financing. This paper has an objective to maximize the total profit by developing a synergic economic order quantity model by considering multi-trade-credit policy, rework, and shortages simultaneously. This model can help in making decisions to enhance the performance of sustainable inventory management by controlling the cycle time and a fraction of time for a global supply chain. A non-derivative approach is employed to develop a closed-form optimal result. The numerical illustration with sensitivity analysis is also drawn to provide managerial insights into real practices.

In a recent paper, Ouyang et al. [10] proposed a (Q, r, L) inventory model with defective items in an arrival lot. The purpose of this study is to generalize Ouyang et al.?s [10] model by allowing setup cost (A) as a decision variable in conjunction with order quantity (Q), reorder point (r) and lead time (L). In this study, we first assume that the lead time demand follows a normal distribution, and then relax this assumption by only assuming that the first two moments of the lead time demand are given. For each case, an algorithm procedure of finding the optimal solution is developed.